Supermat Grocery Sales Analysis Project¶

STEP-1 import important libraries for analyze the data and visualize, cleaning and processing the data¶

In [ ]:
In [1]:
import pandas as pd   # for data cleaning and processing the data
import numpy as np # for linear algebra
import matplotlib.pyplot as plt  #for visualization
import seaborn as sns 
import warnings
warnings.filterwarnings('ignore')  # this for is igore the warning

STEP-2 load the dataset¶

import data in csv¶
In [2]:
df=pd.read_csv("Supermat_Grocery_Sales_dataset.csv")
In [3]:
df.head()
Out[3]:
Order ID Customer Name Category Sub Category City Order Date Region Sales Discount Profit State
0 OD1 Harish Oil & Masala Masalas Vellore 11-08-2017 North 1254.0 0.12 401.28 Tamil Nadu
1 OD2 Sudha Beverages Health Drinks Krishnagiri 11-08-2017 South 749.0 0.18 149.80 Tamil Nadu
2 OD3 Hussain Food Grains Atta & Flour Perambalur 06-12-2017 West 2360.0 0.21 165.20 Tamil Nadu
3 OD4 Jackson Fruits & Veggies Fresh Vegetables Dharmapuri 10-11-2016 South 896.0 0.25 89.60 Tamil Nadu
4 OD5 Ridhesh Food Grains Organic Staples Ooty 10-11-2016 South 2355.0 0.26 918.45 Tamil Nadu
In [4]:
df.columns
Out[4]:
Index(['Order ID', 'Customer Name', 'Category', 'Sub Category', 'City',
       'Order Date', 'Region', 'Sales', 'Discount', 'Profit', 'State'],
      dtype='object')

STEP-3 clean the data and pre-processing the data¶

-> Handling Missing Data¶
->Standardizing data formates¶
-> Handling Duplicates¶
-> Filter unwanted outtliers¶
In [5]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9994 entries, 0 to 9993
Data columns (total 11 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Order ID       9994 non-null   object 
 1   Customer Name  9994 non-null   object 
 2   Category       9994 non-null   object 
 3   Sub Category   8504 non-null   object 
 4   City           8504 non-null   object 
 5   Order Date     8504 non-null   object 
 6   Region         8504 non-null   object 
 7   Sales          8504 non-null   float64
 8   Discount       8504 non-null   float64
 9   Profit         8504 non-null   float64
 10  State          8504 non-null   object 
dtypes: float64(3), object(8)
memory usage: 859.0+ KB
In [6]:
df.shape  # this function is show the row and column
Out[6]:
(9994, 11)
In [7]:
df.duplicated().sum()     # this duplicated function  is show the duplicated value
Out[7]:
0
In [8]:
df.isnull().sum().sum()    # this show tthe null value in data
Out[8]:
11920
In [9]:
(df.isnull().sum().sum()/df.shape[0])*100
Out[9]:
119.27156293776267
In [10]:
df.isnull().sum()
Out[10]:
Order ID            0
Customer Name       0
Category            0
Sub Category     1490
City             1490
Order Date       1490
Region           1490
Sales            1490
Discount         1490
Profit           1490
State            1490
dtype: int64
In [11]:
(df.isnull().sum()/df.shape[0])*100
Out[11]:
Order ID          0.000000
Customer Name     0.000000
Category          0.000000
Sub Category     14.908945
City             14.908945
Order Date       14.908945
Region           14.908945
Sales            14.908945
Discount         14.908945
Profit           14.908945
State            14.908945
dtype: float64
In [12]:
df.dropna(inplace=True)    #drop function is delete the null row
In [13]:
df.shape[0]
Out[13]:
8504

After cleaning¶

In [14]:
(df.isnull().sum()/df.shape[0])*100
Out[14]:
Order ID         0.0
Customer Name    0.0
Category         0.0
Sub Category     0.0
City             0.0
Order Date       0.0
Region           0.0
Sales            0.0
Discount         0.0
Profit           0.0
State            0.0
dtype: float64
In [15]:
df.info()   # info function is show the all column datatype and not null valu

<class 'pandas.core.frame.DataFrame'>
Index: 8504 entries, 0 to 9993
Data columns (total 11 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Order ID       8504 non-null   object 
 1   Customer Name  8504 non-null   object 
 2   Category       8504 non-null   object 
 3   Sub Category   8504 non-null   object 
 4   City           8504 non-null   object 
 5   Order Date     8504 non-null   object 
 6   Region         8504 non-null   object 
 7   Sales          8504 non-null   float64
 8   Discount       8504 non-null   float64
 9   Profit         8504 non-null   float64
 10  State          8504 non-null   object 
dtypes: float64(3), object(8)
memory usage: 797.2+ KB

Convert 'Order Date' to datetime format¶

Extract day, month, and year from 'Order Date'¶

In [16]:
df['Order Date']=pd.to_datetime(df['Order Date'],format='mixed')
In [17]:
df['Order Date'].head()
Out[17]:
0   2017-11-08
1   2017-11-08
2   2017-06-12
3   2016-10-11
4   2016-10-11
Name: Order Date, dtype: datetime64[ns]

After convert datetime¶

In [18]:
df['Order Year']=df['Order Date'].dt.year
df['Order month']=df['Order Date'].dt.month
df['Order day']=df['Order Date'].dt.day
df.head(3)
Out[18]:
Order ID Customer Name Category Sub Category City Order Date Region Sales Discount Profit State Order Year Order month Order day
0 OD1 Harish Oil & Masala Masalas Vellore 2017-11-08 North 1254.0 0.12 401.28 Tamil Nadu 2017 11 8
1 OD2 Sudha Beverages Health Drinks Krishnagiri 2017-11-08 South 749.0 0.18 149.80 Tamil Nadu 2017 11 8
2 OD3 Hussain Food Grains Atta & Flour Perambalur 2017-06-12 West 2360.0 0.21 165.20 Tamil Nadu 2017 6 12
In [19]:
df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 8504 entries, 0 to 9993
Data columns (total 14 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   Order ID       8504 non-null   object        
 1   Customer Name  8504 non-null   object        
 2   Category       8504 non-null   object        
 3   Sub Category   8504 non-null   object        
 4   City           8504 non-null   object        
 5   Order Date     8504 non-null   datetime64[ns]
 6   Region         8504 non-null   object        
 7   Sales          8504 non-null   float64       
 8   Discount       8504 non-null   float64       
 9   Profit         8504 non-null   float64       
 10  State          8504 non-null   object        
 11  Order Year     8504 non-null   int32         
 12  Order month    8504 non-null   int32         
 13  Order day      8504 non-null   int32         
dtypes: datetime64[ns](1), float64(3), int32(3), object(7)
memory usage: 896.9+ KB
In [20]:
df.describe()   # describe functionn is describe the all numerical column
Out[20]:
Order Date Sales Discount Profit Order Year Order month Order day
count 8504 8504.000000 8504.000000 8504.000000 8504.000000 8504.000000 8504.000000
mean 2017-04-30 20:05:48.824082688 1492.189675 0.226646 373.913921 2016.724835 7.797037 15.518109
min 2015-01-03 00:00:00 500.000000 0.100000 25.250000 2015.000000 1.000000 1.000000
25% 2016-05-24 00:00:00 992.000000 0.160000 180.442500 2016.000000 5.000000 8.000000
50% 2017-06-26 00:00:00 1494.000000 0.230000 318.615000 2017.000000 9.000000 16.000000
75% 2018-05-14 00:00:00 1989.250000 0.290000 522.727500 2018.000000 11.000000 23.000000
max 2018-12-30 00:00:00 2500.000000 0.350000 1120.950000 2018.000000 12.000000 31.000000
std NaN 578.301393 0.075038 239.218627 1.120837 3.290614 8.749222
In [21]:
df.drop('Order Date',axis=1,inplace=True)  ## this fumnction is drop the columns
In [22]:
df.head()
Out[22]:
Order ID Customer Name Category Sub Category City Region Sales Discount Profit State Order Year Order month Order day
0 OD1 Harish Oil & Masala Masalas Vellore North 1254.0 0.12 401.28 Tamil Nadu 2017 11 8
1 OD2 Sudha Beverages Health Drinks Krishnagiri South 749.0 0.18 149.80 Tamil Nadu 2017 11 8
2 OD3 Hussain Food Grains Atta & Flour Perambalur West 2360.0 0.21 165.20 Tamil Nadu 2017 6 12
3 OD4 Jackson Fruits & Veggies Fresh Vegetables Dharmapuri South 896.0 0.25 89.60 Tamil Nadu 2016 10 11
4 OD5 Ridhesh Food Grains Organic Staples Ooty South 2355.0 0.26 918.45 Tamil Nadu 2016 10 11
In [46]:
data=df
In [47]:
data = pd.DataFrame(df)
In [49]:
data.to_csv("df.csv", index=False)

-> region_sales , region_profit , region by discount¶

In [ ]:
In [ ]:
In [23]:
# Aggregate data by region
region_sales = df.groupby('Region')['Sales'].sum().reset_index()
region_profit = df.groupby('Region')['Profit'].sum().reset_index()
region_discount = df.groupby('Region')['Discount'].mean().reset_index()

# Merge all metrics into one dataframe
region_summary = region_sales.merge(region_profit, on='Region')
region_summary = region_summary.merge(region_discount, on='Region')

# Rename columns
region_summary.columns = ['Region', 'Total Sales', 'Total Profit', 'Average Discount']

# Display the results
region_summary
Out[23]:
Region Total Sales Total Profit Average Discount
0 Central 2951514.0 730219.49 0.228122
1 East 3570538.0 909880.50 0.227015
2 North 1254.0 401.28 0.120000
3 South 2079498.0 529834.71 0.226190
4 West 4086777.0 1009428.00 0.225514

-> Data is neat and clean and its ready to EDA & visualization then find the insight in Grocessary sales analysis dataset¶

step-4 EDA & Visualization¶

In [24]:
sns.set_theme(style="whitegrid")

# Create figure
plt.figure(figsize=(6, 5))

# Plot value counts with color palette
ax = df['Category'].value_counts().plot(kind='bar', 
                                        color=sns.color_palette("viridis", len(df['Category'].unique())),
                                        edgecolor='black')

# Add labels and title
plt.title("Total Number of Categories", fontsize=14, fontweight='bold')
plt.xlabel("Category", fontsize=12)
plt.ylabel("Count", fontsize=12)
plt.xticks(rotation=45, ha='right')

# Add labels to each bar
for container in ax.containers:
    ax.bar_label(container, fmt='%d', fontsize=10, padding=3)

# Show the plot
plt.show()
No description has been provided for this image

The "Snacks" category has the highest count, indicating it is the most stocked or purchased item.¶

The "Oil & Masala" category has the lowest count, suggesting comparatively lower availability or demand¶

sub-category¶

In [25]:
sns.set_theme(style="whitegrid")

# Create figure
plt.figure(figsize=(6, 5))

# Plot value counts with color palette
ax = df['Sub Category'].value_counts().plot(kind='line' )
                        
# Add labels and title
plt.title("Total Number of Sub Categories", fontsize=14, fontweight='bold')
plt.xlabel("Sub Category", fontsize=12)
plt.ylabel("Count", fontsize=12)
plt.xticks(rotation=45, ha='right')

# Show the plot
plt.show()
No description has been provided for this image

The "Health Drinks" subcategory has the highest count, indicating strong demand or availability.¶

In contrast, "Organic Fruits" have the lowest count, suggesting lower stock or consumer preference.¶

In [26]:
df.columns.values
Out[26]:
array(['Order ID', 'Customer Name', 'Category', 'Sub Category', 'City',
       'Region', 'Sales', 'Discount', 'Profit', 'State', 'Order Year',
       'Order month', 'Order day'], dtype=object)

-> Region by sales¶

In [27]:
# Aggregate sales data by region
region_sales = df.groupby('Region')['Sales'].sum().reset_index()

# Visualization - Sales by Region
plt.figure(figsize=(10, 5))
sns.barplot(x='Region', y='Sales', data=region_sales, palette='Blues')

# Add text labels on bars
for index, row in region_sales.iterrows():
    plt.text(index, row['Sales'], f'{row["Sales"]:.2f}', ha='center', va='bottom')

# Set labels and title
plt.xlabel("Region")
plt.ylabel("Total Sales")
plt.title("Total Sales by Region")
plt.xticks(rotation=45)

# Show plot
plt.show()

# Display the results
region_sales
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Out[27]:
Region Sales
0 Central 2951514.0
1 East 3570538.0
2 North 1254.0
3 South 2079498.0
4 West 4086777.0

The West region has the highest total sales, while the North region has significantly lower sales, indicating a disparity in regional performance.¶

The East and Central regions also show strong sales, but North’s extremely low sales suggest potential data issues or underperformance in that area.¶

-> Region by Profit¶

In [28]:
# Visualization - Profit by Region
plt.figure(figsize=(10, 5))
sns.barplot(x='Region', y='Profit', data=region_profit, palette='Greens')

# Add text labels on bars
for index, row in region_profit.iterrows():
    plt.text(index, row['Profit'],f'{row["Profit"]:.2f}',  ha='center', va='bottom')

# Set labels and title
plt.xlabel("Region")
plt.ylabel("Total Profit")
plt.title("Total Profit by Region")
plt.xticks(rotation=45)

# Show plot
plt.show()

# Display the results
region_profit
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Out[28]:
Region Profit
0 Central 730219.49
1 East 909880.50
2 North 401.28
3 South 529834.71
4 West 1009428.00

The West region generates the highest profit, followed by East and Central, indicating strong financial performance in these areas.¶

The North region shows extremely low profit, suggesting inefficiencies, high costs, or low sales in that region.¶

Region by discount¶

In [29]:
region_Discountt = df.groupby('Region')['Discount'].mean().reset_index()
region_Discountt
Out[29]:
Region Discount
0 Central 0.228122
1 East 0.227015
2 North 0.120000
3 South 0.226190
4 West 0.225514
In [30]:
# Aggregate discount data by region
region_discount = df.groupby('Region')['Discount'].mean().reset_index()

# Visualization - Discount by Region
plt.figure(figsize=(10, 5))
sns.barplot(x='Region', y='Discount', data=region_discount, palette='Reds')

# Add text labels on bars
for index, discount in enumerate(region_discount['Discount']):
    plt.text(index, discount, f'{discount:.2f}', ha='center', va='bottom')

# Set labels and title
plt.xlabel("Region")
plt.ylabel("Average Discount")
plt.title("Average Discount by Region")
plt.xticks(rotation=45)

# Show plot
plt.show()

# Display the results
print(region_discount)
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    Region  Discount
0  Central  0.228122
1     East  0.227015
2    North  0.120000
3    South  0.226190
4     West  0.225514

Central, East, South, and West regions have a similar average discount of 0.23, indicating a uniform discount strategy across these areas.¶

The North region has a significantly lower average discount (0.12), which might be affecting its lower sales and profit performance¶

In [ ]:

-> Region by sales ,profit and discount¶

In [ ]:
In [ ]:
In [31]:
# Aggregate data by region
region_summary = df.groupby('Region').agg({'Sales': 'sum', 'Profit': 'sum','Discount':'mean'}).reset_index()
# Rename columns
region_summary.columns = ['Region', 'Total Sales', 'Total Profit', 'Average Discount']

# Visualization - Merging all metrics into one graph
plt.figure(figsize=(19, 6))

# Set position for bars
bar_width = 0.3
positions = range(len(region_summary['Region']))

# Create bars for each metric
bars1 = plt.bar(positions, region_summary['Total Sales'], width=bar_width, label='Total Sales', color='blue', alpha=0.7)
bars2 = plt.bar([p + bar_width for p in positions], region_summary['Total Profit'], width=bar_width, label='Total Profit', color='green', alpha=0.7)
bars3 = plt.bar([p + 2 * bar_width for p in positions], region_summary['Average Discount'], width=bar_width, label='Average Discount', color='red', alpha=0.8)

# Add text labels on bars
def add_labels(bars):
    for bar in bars:
        height = bar.get_height()
        plt.text(bar.get_x() + bar.get_width() / 2, height, f'{height:.2f}', ha='center', va='bottom')

add_labels(bars1)
add_labels(bars2)
add_labels(bars3)

# Set labels and title
plt.xlabel("Region")
plt.ylabel("Values")
plt.title("Sales, Profit, and Discount by Region",fontsize=14, fontweight='bold')
plt.xticks([p + bar_width for p in positions], region_summary['Region'], rotation=45)
plt.legend()

# Show plot
plt.show()

# Display the results
region_summary
No description has been provided for this image
Out[31]:
Region Total Sales Total Profit Average Discount
0 Central 2951514.0 730219.49 0.228122
1 East 3570538.0 909880.50 0.227015
2 North 1254.0 401.28 0.120000
3 South 2079498.0 529834.71 0.226190
4 West 4086777.0 1009428.00 0.225514

West region leads in both sales (4M+) and profit (1M+), indicating strong market performance despite offering similar discounts as other high-performing regions.¶

North region has extremely low sales (1,254) and profit (401), possibly due to its significantly lower discount (0.12), affecting customer attraction and revenue.¶

Central, East, and South regions maintain a balance with moderate sales, profit, and uniform discounts (0.23), suggesting a consistent pricing strategy.¶

Total Profit and Sales by State¶

In [32]:
# Group data by 'State' and sum 'Sales' and 'Profit'
state_summary = df.groupby('State').agg({'Sales': 'sum', 'Profit': 'sum','Discount':'mean'}).reset_index()

#rename column
state_summary.columns=['State','Total Sales','Total Profit','Average Discount']

state_summary
Out[32]:
State Total Sales Total Profit Average Discount
0 Tamil Nadu 12689581.0 3179763.98 0.226646

Total Profit and Total Sales by Category¶

In [33]:
# Group data by 'Category' and sum 'Sales' and 'Profit'
Category_summary = df.groupby('Category').agg({'Sales': 'sum', 'Profit': 'sum','Discount':'mean'}).reset_index()

#rename the column
Category_summary.columns=['Category','Total Sales','Total Profit','Total Discount']


Category_summary
Out[33]:
Category Total Sales Total Profit Total Discount
0 Bakery 2112281.0 528521.06 0.225428
1 Beverages 2085313.0 525605.76 0.230329
2 Food Grains 2115272.0 529162.64 0.228548
3 Fruits & Veggies 2100727.0 530400.38 0.229295
4 Oil & Masala 2038442.0 497895.29 0.224526
5 Snacks 2237546.0 568178.85 0.222048
In [34]:
# Aggregate sales and profit by category
category_summary = df.groupby("Category")[["Sales", "Profit"]].sum().reset_index()

# Plot the bar chart
fig, ax = plt.subplots(figsize=(10, 6))

ax.plot(category_summary["Category"], category_summary["Sales"], marker='o', linestyle='-', color="blue", label="Sales")
ax.plot(category_summary["Category"], category_summary["Profit"], marker='o', linestyle='-', color="green", label="Profit")

#category_summary.plot(kind="line", x="Category", y=["Sales", "Profit"], ax=ax, color=["blue", "green"])

# Customize the plot
plt.title("Total Sales and Profit by Category")
plt.ylabel("Amount (in currency units)")
plt.xlabel("Category")
plt.xticks(rotation=45)
plt.legend(["Sales", "Profit"])
plt.grid(axis="y", linestyle="--", alpha=0.7)

# Show the plot
plt.show()
No description has been provided for this image

The chart shows total sales and profit by category. Sales are consistently high across categories,¶

while profit varies, suggesting differing cost structures or pricing strategies.¶

"Bakery" and "Snacks" categories appear to have the highest profit margins¶

In [35]:
# Aggregate data by Category
Category_summary = df.groupby('Category').agg({'Sales': 'sum', 'Profit': 'sum','Discount':'mean'}).reset_index()
# Rename columns
Category_summary.columns = ['Category', 'Total Sales', 'Total Profit', 'Average Discount']

# Visualization - Merging all metrics into one graph
plt.figure(figsize=(19, 6))

# Set position for bars
bar_width = 0.3
positions = range(len(Category_summary['Category']))

# Create bars for each metric
bars1 = plt.bar(positions, Category_summary['Total Sales'], width=bar_width, label='Total Sales', color='blue', alpha=0.7)
bars2 = plt.bar([p + bar_width for p in positions], Category_summary['Total Profit'], width=bar_width, label='Total Profit', color='green', alpha=0.7)
bars3 = plt.bar([p + 2 * bar_width for p in positions],Category_summary['Average Discount'], width=bar_width, label='Average Discount', color='red', alpha=0.8)

# Add text labels on bars
def add_labels(bars):
    for bar in bars:
        height = bar.get_height()
        plt.text(bar.get_x() + bar.get_width() / 2, height, f'{height:.2f}', ha='center', va='bottom')

add_labels(bars1)
add_labels(bars2)
add_labels(bars3)

# Set labels and title
plt.xlabel("Category")
plt.ylabel("Values")
plt.title("Sales, Profit, and Discount by Category",fontsize=14, fontweight='bold')
plt.xticks([p + bar_width for p in positions], Category_summary['Category'], rotation=45)
plt.legend()

# Show plot
plt.show()
No description has been provided for this image

Top Sales Category: Snacks have the highest total sales (~2.23M), followed closely by Bakery and Food Grains.¶

Profitability Variation: Although sales are high across categories, profit margins vary, with Bakery and Snacks leading in profits.¶

Zero Sales & Profit: The "Eggs" category shows zero sales and profit, possibly indicating missing data or lack of sales.¶

Balanced Performance: Categories like Fruits & Veggies and Oil & Masala have steady sales and moderate profits.¶

In [ ]:
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Sub Category by sales and profit¶

In [ ]:
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In [36]:
# Group data by 'Sub Category' and sum 'Sales' and 'Profit'
Sub_Category_summary = df.groupby('Sub Category').agg({'Sales': 'sum', 'Profit': 'sum','Discount':'mean'}).reset_index()

#rename the column
Sub_Category_summary.columns=['Sub Category','Total Sales','Total Profit','Total Discount']
Sub_Category_summary.head(4)
Out[36]:
Sub Category Total Sales Total Profit Total Discount
0 Atta & Flour 534649.0 127861.10 0.227875
1 Biscuits 684083.0 169357.62 0.225033
2 Breads & Buns 742586.0 190764.98 0.226494
3 Cakes 685612.0 168398.46 0.224646
In [37]:
sub_category_summary = df.groupby("Sub Category")[["Sales", "Profit"]].sum().reset_index()

# Plot the bar chart
fig, ax = plt.subplots(figsize=(10, 6))
sub_category_summary.plot(kind="line", x="Sub Category", y=["Sales", "Profit"] ,ax=ax, color=["blue", "green"])

# Customize the plot
plt.title("Total Sales and Profit by  sub Category")
plt.ylabel("Amount (in currency units)")
plt.xlabel("Sub Category")
plt.xticks(rotation=45)
plt.legend(["Sales", "Profit"])
plt.grid(axis="y", linestyle="--", alpha=0.7)

# Show the plot
plt.show
Out[37]:
<function matplotlib.pyplot.show(close=None, block=None)>
No description has been provided for this image

Sales Peaks: Certain subcategories, such as "Health Drinks" and "Organic Vegetables," have significantly high sales compared to others.¶

Profit Trends: Although sales fluctuate, profit remains relatively stable across subcategories, with minor variations.¶

High Sales, Low Profit: Some subcategories show high sales but comparatively lower profits, indicating possible high costs or discounts.¶

Growth Opportunities: Categories with lower profits despite high sales may need pricing or cost optimization for better profitability¶

In [ ]:
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City by sale and profit¶

In [38]:
# Group data by city and calculate total sales and profit
City_summary = df.groupby("City")[["Sales", "Profit"]].sum().reset_index()

# Plot the scatter chart
fig, ax = plt.subplots(figsize=(12, 6))

# Scatter plot for Sales
ax.scatter(City_summary["City"], City_summary["Sales"], color="blue", label="Sales")

# Scatter plot for Profit
ax.scatter(City_summary["City"], City_summary["Profit"], color="green", label="Profit")

# Customize the plot
plt.title("Total Sales and Profit by City")
plt.ylabel("Amount (in currency units)")
plt.xlabel("City")
plt.xticks(rotation=45)
plt.legend()
plt.grid(axis="y", linestyle="--", alpha=0.7)

# Show the plot
plt.show()
No description has been provided for this image

Cities with higher sales do not always have higher profits, indicating variations in profitability across locations.¶

ome cities show significant profit despite lower sales, suggesting better margins or cost efficiencies.¶

In [ ]:
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In [39]:
print(df.columns)

Index(['Order ID', 'Customer Name', 'Category', 'Sub Category', 'City',
       'Region', 'Sales', 'Discount', 'Profit', 'State', 'Order Year',
       'Order month', 'Order day'],
      dtype='object')
In [40]:
df['Order month'].unique()
Out[40]:
array([11,  6, 10,  4, 12,  5,  8,  7,  1,  9,  3,  2])

Order year by sales , profit and Discount¶

In [41]:
# Group by Order Year and sum Sales, Profit, and Discount
yearly_summary = df.groupby("Order Year")[["Sales", "Profit", "Discount"]].sum().reset_index()

# Create Pie Charts
fig, axes = plt.subplots(1, 3, figsize=(18, 6))

# Sales Pie Chart
axes[0].pie(yearly_summary["Sales"], labels=yearly_summary["Order Year"], autopct='%1.1f%%', colors=['blue', 'lightblue'])
axes[0].set_title("Sales by Year")

# Profit Pie Chart
axes[1].pie(yearly_summary["Profit"], labels=yearly_summary["Order Year"], autopct='%1.1f%%', colors=['green', 'lightgreen'])
axes[1].set_title("Profit by Year")

# Discount Pie Chart
axes[2].pie(yearly_summary["Discount"], labels=yearly_summary["Order Year"], autopct='%1.1f%%', colors=['red', 'lightcoral'])
axes[2].set_title("Discount by Year")

# Show Plot
plt.tight_layout()
plt.show()
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Sales, profit, and discounts all show a consistent increase over the years, with 2018 contributing the highest share in all three categories.¶

The year 2017 also had a significant share, indicating strong business performance before reaching its peak in 2018.¶

The lowest share is observed in 2015 across all metrics, suggesting a steady growth trend in sales, profit, and discount allocation over time.¶

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sales and profit by order year¶

In [42]:
import pandas as pd
import plotly.express as px

# Grouping data by Order Year
yearly_summary = df.groupby("Order Year")[["Sales", "Profit"]].sum().reset_index()

# Create Bar Chart
fig = px.bar(
    yearly_summary, 
    x="Order Year", 
    y=["Sales", "Profit"], 
    barmode="group",  # Group bars side by side
    title="Total Sales and Profit by Order Year",
    labels={"value": "Amount (in currency units)", "Order Year": "Year"},
    color_discrete_map={"Sales": "blue", "Profit": "green"}
)

# Show figure
fig.show()

The bar chart shows a steady increase in both sales and profit from 2015 to 2018, indicating business growth. However,¶

the profit margin remains relatively low compared to sales, suggesting potential cost or pricing optimizations¶

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Sales and profit by order month¶

In [43]:
# Group data by Order Month
Order_month_summary = df.groupby("Order month")[["Sales", "Profit"]].sum().reset_index()

# Plot the line chart
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(Order_month_summary["Order month"], Order_month_summary["Sales"], marker='o', linestyle='-', color="blue", label="Sales")
ax.plot(Order_month_summary["Order month"], Order_month_summary["Profit"], marker='o', linestyle='-', color="green", label="Profit")

# Customize the plot
plt.title("Total Sales and Profit by Order Month")
plt.ylabel("Amount (in currency units)")
plt.xlabel("Order Month")
plt.xticks(Order_month_summary["Order month"])  # Ensure proper month ticks
plt.legend()
plt.grid(axis="y", linestyle="--", alpha=0.7)

# Show the plot
plt.show()
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Sales and profit exhibit a strong upward trend from August to December, with peaks in September and November.¶

February has the lowest sales and profit, suggesting weaker demand during this period.¶

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sales and profit by order day¶

In [44]:
# Group data by Order day
Order_day_summary = df.groupby("Order day")[["Sales", "Profit"]].sum().reset_index()

# Plot the line chart
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(Order_day_summary["Order day"], Order_day_summary["Sales"], marker='o', linestyle='-', color="blue", label="Sales")
ax.plot(Order_day_summary["Order day"], Order_day_summary["Profit"], marker='o', linestyle='-', color="green", label="Profit")

# Customize the plot
plt.title("Total Sales and Profit by Order day")
plt.ylabel("Amount (in currency units)")
plt.xlabel("Order day")
plt.xticks(Order_month_summary["Order day"])  # Ensure proper month ticks
plt.legend()
plt.grid(axis="y", linestyle="--", alpha=0.7)

# Show the plot
plt.show()

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File C:\ProgramData\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:3805, in Index.get_loc(self, key)
   3804 try:
-> 3805     return self._engine.get_loc(casted_key)
   3806 except KeyError as err:

File index.pyx:167, in pandas._libs.index.IndexEngine.get_loc()

File index.pyx:196, in pandas._libs.index.IndexEngine.get_loc()

File pandas\\_libs\\hashtable_class_helper.pxi:7081, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas\\_libs\\hashtable_class_helper.pxi:7089, in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'Order day'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
Cell In[44], line 13
     11 plt.ylabel("Amount (in currency units)")
     12 plt.xlabel("Order day")
---> 13 plt.xticks(Order_month_summary["Order day"])  # Ensure proper month ticks
     14 plt.legend()
     15 plt.grid(axis="y", linestyle="--", alpha=0.7)

File C:\ProgramData\anaconda3\Lib\site-packages\pandas\core\frame.py:4102, in DataFrame.__getitem__(self, key)
   4100 if self.columns.nlevels > 1:
   4101     return self._getitem_multilevel(key)
-> 4102 indexer = self.columns.get_loc(key)
   4103 if is_integer(indexer):
   4104     indexer = [indexer]

File C:\ProgramData\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:3812, in Index.get_loc(self, key)
   3807     if isinstance(casted_key, slice) or (
   3808         isinstance(casted_key, abc.Iterable)
   3809         and any(isinstance(x, slice) for x in casted_key)
   3810     ):
   3811         raise InvalidIndexError(key)
-> 3812     raise KeyError(key) from err
   3813 except TypeError:
   3814     # If we have a listlike key, _check_indexing_error will raise
   3815     #  InvalidIndexError. Otherwise we fall through and re-raise
   3816     #  the TypeError.
   3817     self._check_indexing_error(key)

KeyError: 'Order day'
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Sales and profit show fluctuations throughout the month, with peaks around the 20th and 21st, indicating higher purchasing activity.¶

The end of the month experiences a decline in both sales and profit, suggesting lower consumer spending during this period¶

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